EnsembleSVM: A Library for Ensemble Learning Using Support Vector Machines

TL;DR

EnsembleSVM is a software library that enables efficient ensemble learning with SVMs, reducing training complexity while maintaining high accuracy, and is freely available online.

Contribution

It introduces a novel implementation that avoids duplicate storage of support vectors, improving efficiency in ensemble SVM training.

Findings

Ensemble methods significantly reduce training complexity.

High predictive accuracy is maintained with ensemble approaches.

The software is freely accessible online.

Abstract

EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models. It currently offers ensemble methods based on binary SVM models. Our implementation avoids duplicate storage and evaluation of support vectors which are shared between constituent models. Experimental results show that using ensemble approaches can drastically reduce training complexity while maintaining high predictive accuracy. The EnsembleSVM software package is freely available online at http://esat.kuleuven.be/stadius/ensemblesvm.